We aim to provide an algorithm to predict the distribution of the critical times of financial bubbles employing a log-periodic power law. Our approach consists of a constrained genetic algorithm and an improved price gyration method, which generates an initial population of parameters using historical data for the genetic algorithm. The key enhancements of price gyration algorithm are (i) different window sizes for peak detection and (ii) a distance-based weighting approach for peak selection. Our results show a significant improvement in the prediction of financial crashes. The diagnostic analysis further demonstrates the accuracy, efficiency, and stability of our predictions.
Bibliographical noteFunding Information:
This work was supported by KAIST through the 4th Industrial Revolution Project and Moon Soul Graduate School of Future Strategy (Kwangwon Ahn), and by Peking University HSBC Business School through Bairui Trust Research Fund (Kwangwon Ahn). In addition, the authors would like to thank Kyungmoo Heo, Hanwool Jang, and Guseon Ji for their excellent research assistant work.
© 2018 Bingcun Dai et al.
All Science Journal Classification (ASJC) codes
- Computer Science(all)